1. Technical Field
The present disclosure relates to the field of active noise control. In particular, to a system for active noise equalization.
2. Related Art
Significant improvements in fuel-efficiency of vehicles have been achieved through adoption of new engine management and drive-train technologies. Examples include cylinder deactivation and lower engine speed (a.k.a. lower RPM) torque. In some cases these technologies result in objectionable interior cabin noise, otherwise known as “boom”, as a result of increased vibrational energy produced in the engine and/or drive-train being transmitted into acoustic energy inside the cabin. More conventional engine management and drive-train technologies may also lead to objectionable interior cabin noise, however, these newer technologies tend to exacerbate the problem. The objectionable engine noise is typically low-frequency (for example, less than 100 Hz), tonal (one or more tones which may be harmonically related), and with frequency proportional to engine speed. Vehicle noise characteristics are a quality factor in the overall driving experience, and interior sound quality is often marketed as a strong selling point. There are also potential health and safety implications of interior cabin noise. Long-term exposure to high-amplitude low-frequency noise (LFN), particularly in the infrasonic range (below 20 Hz), has been linked to vibro-acoustic disease. Vibro-acoustic disease has been observed amongst aircraft technicians, commercial and military pilots and cabin crew-members, ship machinists, restaurant workers, and disk-jockeys. LFN is also a cause of driver fatigue in cars, trucks, and buses.
Passive noise control methods are used to reduce interior cabin noise. This approach includes the use of acoustically absorptive and damping materials and the use of deflectors/baffles to reflect sound energy away from the cabin interior. However, there are many disadvantages of passive noise control approaches. For example, passive noise control materials add weight to the vehicle, thus reducing fuel efficiency. The approach is also costly, in terms of raw materials, time and effort to incorporate these stages into the production line. Furthermore, the effectiveness of passive noise control is reduced as the frequency of the disturbance is lowered, such that only the most expensive and impractical of passive noise control mechanisms would be effective at 50 Hz, for example.
Another means to reduce interior cabin noise is active vibration control, for example, active engine mounts, which compensate for vibrations introduced into the chassis via the engine mount by providing controlled energy to the mounting system. Active engine mounts consist of passive mounts, force generating actuators, sensors, and electronic controllers, and may provide superior vibration isolation capabilities compared to conventional passive elastomeric and hydraulic engine mounts. The superior vibration isolation capabilities of active mounts may also allow for the elimination of an engine balancer shaft, reducing engine weight, height, and cost, and helping to achieve fuel efficiency. There are various limitations and trade-offs that must be made in selecting an active vibration control mechanism: bandwidth, response time, displacement, efficiency, effectiveness, stiffness, weight, size and realizable force. These approaches typically require dedicated actuators, controllers and sensors, so there is a significant expense in manufacturing these systems.
Given that the characteristics of automotive engine noise are typically low-frequency, tonal, and predictable (assuming engine speed is known), active noise control (ANC) is suited to the task of actively reducing the noise inside the vehicle cabin. The basic principle of these systems is that a primary acoustic noise component may be cancelled at a given location by superimposing a secondary acoustic component or “anti-noise” component of equal amplitude but opposite phase. An ANC system requires sensors (e.g. microphones or accelerometers), actuators (e.g. loudspeakers, subwoofers, electrostatic transducer panels), and one or more controller modules. Automotive ANC systems have reused existing audio or infotainment system hardware such as loudspeakers, amplifiers and analog-digital converters, to reduce cost of implementation. However, current commercial ANC systems rely on separate and dedicated hardware controller modules and dedicated input sensors/microphones. These components have a significant cost, the process of integrating the ANC solution with the audio system requires significant integration, wiring and tuning effort, offers little extensibility, provides no easy solution to managing audio power headroom, are expensive to replace, and place restrictions on after-market modifications of the audio system.
FIG. 1 is a block diagram of a single-frequency active noise equalizer (ANE). Whereas the design of an ANC system usually pursues maximal attenuation of the incoming noise, an ANE system 100 reduces the engine noise, created in a noise source 102, to a desired level, or in some cases, may even be used to amplify the engine noise. This can be used to provide the driver with audible feedback related to the engine operation, to allow safe operation of the vehicle, or simply to improve the driver's enjoyment. The desired level of noise can be specified a priori using, for example, a spectral template.
A sine-wave generator 104, may be used to generate a sinusoidal reference signal referred to as a noise model 122, x0(n)=A cos(ω0n), where A and ω0 are the amplitude and frequency of the noise model 122, respectively. ω0 may be synchronized to the engine speed, which may be obtained as a sync signal 120, for example, from a tachometer or directly from the vehicle's Engine Control Unit (ECU). For example, ω0 may be a multiple of the engine cylinder firing frequency. The noise model 122 x0(n) is passed through a 90° phase-shifter 106, which may be a time-domain filter that delays the phase of the noise model 122 to produce x1(n)=A sin(ω0n). Adaptive gains g0(n) and g1(n) are applied to x0(n) and x1(n), respectively, and the results are summed to produce y(n). y(n) is multiplied by an adaptive gain (1-β) 108, producing the control output signal 124, which is sent to an actuator to produce the “anti-noise” or cancelling signal. P(z) 110 and S(z) 112 are the actual primary and secondary path transfer functions, respectively. The output of the primary transfer function P(z) 110 may represent a sound field in the acoustic space containing the primary acoustic noise component associated with the noise source 102. The output of the secondary path transfer function S(z) 112 may represent a sound field in the acoustic space containing the control output signal 124 referred to as the “anti-noise” or cancelling signal. g0(n) and g1(n) may be adapted using an adaptive filtering algorithm 114, such as for example, a least-mean-square (LMS), a normalized LMS (NLMS), affine projection or a recursive least-square (RLS). Two inputs to the adaptive filter are x0(n) and x1(n) each filtered by a time-domain estimate of the secondary path transfer function Ŝ(z) 116. A third input is e′(n), which is a pseudo-error signal 128 obtained by subtracting the output of the balancing branch from an error microphone/sensor signal e(n) 126. The balancing branch includes scaling y(n) using an adaptive balancing gain 118, β, and a time-domain filtering operation using the estimate of the secondary transfer function Ŝ(z) 116. The error microphone/sensor signal e(n) 126 may capture an audio signal representing a sound field in the acoustic space containing any one or more of the primary acoustic noise component, the cancelling signal and other environmental noise.
There are at least three time-domain filtering operations by Ŝ(z) 116 per sample in the ANE 100. A reasonable estimate of the impulse response of the secondary path transfer function may be around 100 ms in duration for an automotive interior, or 100 samples at a nominal sample rate of 1 kHz. The complexity rapidly expands for a multiple-frequency multiple-channel system. For example, for J=3, K=4 and M=5, the number of multiply operations arising solely from secondary path filtering is at least 3*100*3*4*5=18000 per sample, or equivalently 18 MHz, which may be a significant burden on available computing power. Furthermore, the hardware on which the ANE system is running may have memory limitations that do not allow for storage of lengthy secondary path impulse responses. For example, for the same system exemplified above, 3*4*100*4=4.8 kB would be required to store the impulse responses in single precision floating-point (4 bytes/word). In addition, memory is required to store past values of the input signals to the secondary path filters, for example, x(n), x(n−1), . . . , x(n−99). Estimates of the secondary path transfer functions may be obtained using offline or online secondary path modeling, for example, by injecting random noise into each control output and adapting a secondary path impulse response estimate using LMS to minimize the difference between the actual and predicted signal at each error microphone.
Thus the known methods for ANE have significant memory requirements and computational complexity.
Other systems, methods, features and advantages will be, or will become, apparent to one with skill in the art upon examination of the figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included with this description and be protected by the claims that follow.